What Do We Know About Group Health Coaching and Cancer Survivorship? A Scoping Review
Bibliographic record
Abstract
Group health coaching (GHC) may be a suitable method for supporting healthy lifestyle behaviors in cancer patients and survivors. The aim of this scoping review was to explore GHC interventions targeting this population, specifically examining program composition and measured outcomes. A systematic search strategy was used to identify intervention studies focused on GHC with cancer patients and survivors. Seven studies met the criteria. Studies focused on physical activity, diet, weight loss, or some combination thereof utilizing GHC by itself or as one component of an exercise and/or diet intervention. There was a wide range of measured outcomes, grouped into: feasibility/acceptability; physical activity/exercise; body composition and biomarkers; diet; distress, quality of life, fatigue; and other. Overall, studies were found to be feasible and showed positive results for weight loss, diet, and quality of life. Findings for changes in physical activity, distress, and fatigue were mixed. Additionally, variability was found in many of the GHC components. This review suggests GHC for cancer patients and survivors is still in the nascent stages. However, these studies were deemed feasible and satisfactory to participants, with positive outcomes noted. While still in the early stages, GHC appears promising for supporting positive lifestyle behaviors in this population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".